| ▲ | no_wizard a day ago |
| >internal concepts, the model is not aware that it's doing anything so how could it "explain itself" This in a nutshell is why I hate that all this stuff is being labeled as AI. Its advanced machine learning (another term that also feels inaccurate but I concede is at least closer to whats happening conceptually) Really, LLMs and the like still lack any model of intelligence. Its, in the most basic of terms, algorithmic pattern matching mixed with statistical likelihoods of success. And that can get things really really far. There are entire businesses built on doing that kind of work (particularly in finance) with very high accuracy and usefulness, but its not AI. |
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| ▲ | johnecheck a day ago | parent | next [-] |
| While I agree that LLMs are hardly sapient, it's very hard to make this argument without being able to pinpoint what a model of intelligence actually is. "Human brains lack any model of intelligence. It's just neurons firing in complicated patterns in response to inputs based on what statistically leads to reproductive success" |
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| ▲ | whilenot-dev a day ago | parent | next [-] | | What's wrong with just calling them smart algorithmic models? Being smart allows somewhat to be wrong, as long as that leads to a satisfying solution. Being intelligent on the other hand requires foundational correctness in concepts that aren't even defined yet. EDIT: I also somewhat like the term imperative knowledge (models) [0] [0]: https://en.wikipedia.org/wiki/Procedural_knowledge | | |
| ▲ | jfengel a day ago | parent [-] | | The problem with "smart" is that they fail at things that dumb people succeed at. They have ludicrous levels of knowledge and a jaw dropping ability to connect pieces while missing what's right in front of them. The gap makes me uncomfortable with the implications of the word "smart". It is orthogonal to that. | | |
| ▲ | sigmoid10 15 hours ago | parent | next [-] | | >they fail at things that dumb people succeed at Funnily enough, you can also observe that in humans. The number of times I have observed people from highly intellectual, high income/academic families struggle with simple tasks that even the dumbest people do with ease is staggering. If you're not trained for something and suddenly confronted with it for the first time, you will also in all likelihood fail. "Smart" is just as ill-defined as any other clumsy approach to define intelligence. | |
| ▲ | nradov 10 hours ago | parent | prev [-] | | Bombs can be smart, even though they sometimes miss the target. |
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| ▲ | no_wizard a day ago | parent | prev | next [-] | | That's not at all on par with what I'm saying. There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior. We shouldn't seek to muddy this. EDIT: Generally its accepted that a core trait of intelligence is an agent’s ability to achieve goals in a wide range of environments. This means you must be able to generalize, which in turn allows intelligent beings to react to new environments and contexts without previous experience or input. Nothing I'm aware of on the market can do this. LLMs are great at statistically inferring things, but they can't generalize which means they lack reasoning. They also lack the ability to seek new information without prompting. The fact that all LLMs boil down to (relatively) simple mathematics should be enough to prove the point as well. It lacks spontaneous reasoning, which is why the ability to generalize is key | | |
| ▲ | byearthithatius a day ago | parent | next [-] | | "There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior" not really. The whole point they are trying to make is that the capability of these models IS ALREADY muddying the definition of intelligence. We can't really test it because the distribution its learned is so vast. Hence why he have things like ARC now. Even if its just gradient descent based distribution learning and there is no "internal system" (whatever you think that should look like) to support learning the distribution, the question is if that is more than what we are doing or if we are starting to replicate our own mechanisms of learning. | | |
| ▲ | jdhwosnhw a day ago | parent | next [-] | | Peoples’ memories are so short. Ten years ago the “well accepted definition of intelligence” was whether something could pass the Turing test. Now that goalpost has been completely blown out of the water and people are scrabbling to come up with a new one that precludes LLMs. A useful definition of intelligence needs to be measurable, based on inputs/outputs, not internal state. Otherwise you run the risk of dictating how you think intelligence should manifest, rather than what it actually is. The former is a prescription, only the latter is a true definition. | | |
| ▲ | fc417fc802 a day ago | parent | next [-] | | I frequently see this characterization and can't agree with it. If I say "well I suppose you'd at least need to do A to qualify" and then later say "huh I guess A wasn't sufficient, looks like you'll also need B" that is not shifting the goalposts. At worst it's an incomplete and ad hoc specification. More realistically it was never more than an educated guess to begin with, about something that didn't exist at the time, still doesn't appear to exist, is highly subjective, lacks a single broadly accepted rigorous definition to this very day, and ultimately boils down to "I'll know it when I see it". I'll know it when I see it, and I still haven't seen it. QED | | |
| ▲ | jdhwosnhw a day ago | parent [-] | | > If I say "well I suppose you'd at least need to do A to qualify" and then later say "huh I guess A wasn't sufficient, looks like you'll also need B" that is not shifting the goalposts. I dunno, that seems like a pretty good distillation of what moving the goalposts is. > I’ll know it when I see it, and I haven’t seen it. QED While pithily put, thats not a compelling argument. You feel that LLMs are not intelligent. I feel that they may be intelligent. Without a decent definition of what intelligence is, the entire argument is silly. | | |
| ▲ | fc417fc802 21 hours ago | parent | next [-] | | Shifting goalposts usually (at least in my understanding) refers to changing something without valid justification that was explicitly set in a previous step (subjective wording I realize - this is off the top of my head). In an adversarial context it would be someone attempting to gain an advantage by subtly changing a premise in order to manipulate the conclusion. An incomplete list, in contrast, is not a full set of goalposts. It is more akin to a declared lower bound. I also don't think it to applies to the case where the parties are made aware of a change in circumstances and update their views accordingly. > You feel that LLMs are not intelligent. I feel that they may be intelligent. Weirdly enough I almost agree with you. LLMs have certainly challenged my notion of what intelligence is. At this point I think it's more a discussion of what sorts of things people are referring to when they use that word and if we can figure out an objective description that distinguishes those things from everything else. > Without a decent definition of what intelligence is, the entire argument is silly. I completely agree. My only objection is to the notion that goalposts have been shifted since in my view they were never established in the first place. | |
| ▲ | Jensson 17 hours ago | parent | prev [-] | | > I dunno, that seems like a pretty good distillation of what moving the goalposts is. Only if you don't understand what "the goalposts" means. The goalpost isn't "pass the turing test", the goalpost is "manage to do all the same kind of intellectual tasks that humans are", nobody has moved that since the start in the quest for AI. |
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| ▲ | Retric 17 hours ago | parent | prev | next [-] | | LLM’s can’t pass an unrestricted Touring test. LLM’s can mimic intelligence, but if you actually try and exploit their limitations the deception is still trivial to unmask. Various chat bots have long been able to pass more limited versions of a Touring test. The most extreme constraint allows for simply replaying a canned conversation which with a helpful human assistant makes it indistinguishable from a human. But exploiting limitations on a testing format doesn’t have anything to do with testing for intelligence. | |
| ▲ | travisjungroth a day ago | parent | prev [-] | | I’ve realized while reading these comments my opinions on LLMs being intelligent has significantly increased. Rather than argue any specific test, I believe no one can come up with a text-based intelligence test that 90% of literate adults can pass but the top LLMs fail. This would mean there’s no definition of intelligence you could tie to a test where humans would be intelligent but LLMs wouldn’t. A maybe more palatable idea is that having “intelligence” as a binary is insufficient. I think it’s more of an extremely skewed distribution. With how humans are above the rest, you didn’t have to nail the cutoff point to get us on one side and everything else on the other. Maybe chimpanzees and dolphins slip in. But now, the LLMs are much closer to humans. That line is harder to draw. Actually not possible to draw it so people are on one side and LLMs on the other. | | |
| ▲ | fc417fc802 a day ago | parent | next [-] | | Why presuppose that it's possible to test intelligence via text? Most humans have been illiterate for most of human history. I don't mean to claim that it isn't possible, just that I'm not clear why we should assume that it is or that there would be an obvious way of going about it. | | |
| ▲ | travisjungroth a day ago | parent [-] | | Seems pretty reasonable to presuppose this when you filter to people who are literate. That’s darn near a definition of literate, that you can engage with the text intelligently. | | |
| ▲ | fc417fc802 21 hours ago | parent [-] | | I thought the definition of literate was "can interpret text in place of the spoken word". At which point it's worth noting that text is a much lower bandwidth channel than in person communication. Also worth noting that, ex, a mute person could still be considered intelligent. Is it necessarily the case that you could discern general intelligence via a test with fixed structure, known to all parties in advance, carried out via a synthesized monotone voice? I'm not saying "you definitely can't do that" just that I don't see why we should a priori assume it to be possible. Now that likely seems largely irrelevant and out in the weeds and normally I would feel that way. But if you're going to suppose that we can't cleanly differentiate LLMs from humans then it becomes important to ask if that's a consequence of the LLMs actually exhibiting what we would consider general intelligence versus an inherent limitation of the modality in which the interactions are taking place. Personally I think it's far more likely that we just don't have very good tests yet, that our working definition of "general intelligence" (as well as just "intelligence") isn't all that great yet, and that in the end many humans who we consider to exhibit a reasonable level of such will nonetheless fail to pass tests that are based solely on an isolated exchange of natural language. | | |
| ▲ | tsimionescu 18 hours ago | parent [-] | | I generally agree with your framing, I'll just comment on a minor detail about what "literate" means. Typically, people are classed in three categories of literacy, not two: illiterate means you essentially can't read at all, literate means you can read and understand text to some level, but then there are people who are functionally illiterate - people who can read the letters and sound out text, but can't actively comprehend what they're reading to a level that allows them to function normally in society - say, being able to read and comprehend an email they receive at work or a news article. This difference between literate and functionally illiterate may have been what the poster above was referring to. Note that functional illiteracy is not some niche phenomenon, it's a huge problem in many school systems. In my own country (Romania), while the rate of illiteracy is something like <1% of the populace, the rate of functional illiteracy is estimated to be as high as 45% of those finishing school. |
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| ▲ | nl a day ago | parent | prev [-] | | Or maybe accept that LLMs are intelligent and it's human bias that is the oddity here. | | |
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| ▲ | dingnuts a day ago | parent | prev [-] | | How does an LLM muddy the definition of intelligence any more than a database or search engine does? They are lossy databases with a natural language interface, nothing more. | | |
| ▲ | tibbar a day ago | parent | next [-] | | Ah, but what is in the database? At this point it's clearly not just facts, but problem-solving strategies and an execution engine. A database of problem-solving strategies which you can query with a natural language description of your problem and it returns an answer to your problem... well... sounds like intelligence to me. | | |
| ▲ | uoaei a day ago | parent [-] | | > problem-solving strategies and an execution engine Extremely unfounded claims. See: the root comment of this tree. | | |
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| ▲ | madethisnow a day ago | parent | prev [-] | | datasets and search engines are deterministic. humans, and llms are not. | | |
| ▲ | semiquaver a day ago | parent | next [-] | | LLMs are completely deterministic. Their fundamental output is a vector representing a probability distribution of the next token given the model weights and context. Given the same inputs an identical output vector will be produced 100% of the time. This fact is relied upon by for example https://bellard.org/ts_zip/ a lossless compression system that would not work if LLMs were nondeterministic. In practice most LLM systems use this distribution (along with a “temperature” multiplier) to make a weighted random choice among the tokens, giving the illusion of nondeterminism. But there’s no fundamental reason you couldn’t for example always choose the most likely token, yielding totally deterministic output. This is an excellent and accessible series going over how transformer systems work if you want to learn more. https://youtu.be/wjZofJX0v4M | | |
| ▲ | frozenseven 20 hours ago | parent | next [-] | | >In practice most LLM systems use this distribution (along with a “temperature” multiplier) to make a weighted random choice among the tokens In other words, LLMs are not deterministic in just about any real setting. What you said there only compounds with MoE architectures, variable test-time compute allocation, and o3-like sampling. | |
| ▲ | spunker540 21 hours ago | parent | prev [-] | | i've heard it actually depends on the model / hosting architecture. some are not deterministic at the numeric level because there is so much floating point math going on in distributed fashion across gpus, with unpredictable rounding/syncing across machines |
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| ▲ | hatefulmoron a day ago | parent | prev | next [-] | | The LLM's output is chaotic relative to the input, but it's deterministic right? Same settings, same model, same input, .. same output? Where does the chain get broken here? | | |
| ▲ | tsimionescu 17 hours ago | parent | next [-] | | Depends on what you mean specifically by the output. The actual neural network will produce deterministic outputs that could be interpreted as probability values for various tokens. But the interface you'll commonly see used in front of these models will then non-deterministiclaly choose a single next token to output based on those probabilities. Then, this single randomly chosen output is fed back into the network to produce another token, and this process repeats. I would ultimately call the result non-deterministic. You could make it deterministic relatively easily by having a deterministic process for choosing a single token from all of the outputs of the NN (say, always pick the one with the highest weight, and if there are multiple with the same weight, pick the first one in token index order), but no one normally does this, because the results aren't that great per my understanding. | | |
| ▲ | fc417fc802 15 hours ago | parent [-] | | You can have the best of both worlds with something like weighted_selection( output, hash( output ) ) using the hash as the PRNG seed. (If you're paranoid about statistical issues due to identical outputs (extremely unlikely) then add a nonce to the hash.) |
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| ▲ | fc417fc802 a day ago | parent | prev [-] | | Now compare a human to an LSTM with persistent internal state that you can't reset. | | |
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| ▲ | daveguy a day ago | parent | prev [-] | | The only reason LLMs are stochastic instead of deterministic is a random number generator. There is nothing inherently non-deterministic about LLM algorithms unless you turn up the "temperature" of selecting the next word. The fact that determinism can be changed by turning a knob is clear evidence that they are closer to a database or search engine than a human. | | |
| ▲ | travisjungroth a day ago | parent [-] | | You can turn the determinism knob on humans. Psychedelics are one method. | | |
| ▲ | mrob a day ago | parent [-] | | I think that's more adjusting the parameters of the built-in denoising and feature detection circuits of the inherently noisy analog computer that is the brain. |
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| ▲ | david-gpu a day ago | parent | prev | next [-] | | > There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior. Go on. We are listening. | |
| ▲ | nmarinov a day ago | parent | prev | next [-] | | I think the confusion is because you're referring to a common understanding of what AI is but I think the definition of AI is different for different people. Can you give your definition of AI? Also what is the "generally accepted baseline definition for what crosses the threshold of intelligent behavior"? | |
| ▲ | voidspark a day ago | parent | prev | next [-] | | You are doubling down on a muddled vague non-technical intuition about these terms. Please tell us what that "baseline definition" is. | |
| ▲ | appleorchard46 a day ago | parent | prev | next [-] | | > Generally its accepted that a core trait of intelligence is an agent’s ability to achieve goals in a wide range of environments. Be that as it may, a core trait is very different from a generally accepted threshold. What exactly is the threshold? Which environments are you referring to? How is it being measured? What goals are they? You may have quantitative and unambiguous answers to these questions, but I don't think they would be commonly agreed upon. | |
| ▲ | highfrequency a day ago | parent | prev | next [-] | | What is that baseline threshold for intelligence? Could you provide concrete and objective results, that if demonstrated by a computer system would satisfy your criteria for intelligence? | | |
| ▲ | no_wizard a day ago | parent [-] | | see the edit. boils down to the ability to generalize, LLMs can't generalize. I'm not the only one who holds this view either. Francois Chollet, a former intelligence researcher at Google also shares this view. | | |
| ▲ | highfrequency a day ago | parent | next [-] | | Are you able to formulate "generalization" in a concrete and objective way that could be achieved unambiguously, and is currently achieved by a typical human? A lot of people would say that LLMs generalize pretty well - they certainly can understand natural language sequences that are not present in their training data. | | |
| ▲ | whilenot-dev 13 hours ago | parent [-] | | > A lot of people would say that LLMs generalize pretty well What do you mean here? The trained model, the inference engine, is the one that makes an LLM for "a lot of people". > they certainly can understand natural language sequences that are not present in their training data Keeping the trained model as LLM in mind, I think learning a language includes generalization and is typically achieved by a human, so I'll try to formulate: Can a trained LLM model learn languages that hasn't been in its training set just by chatting/prompting? Given that any Korean texts were excluded from the training set, could Korean be learned? Does that even work with languages descending from the same language family (Spanish in the training set but Italian should be learned)? |
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| ▲ | voidspark a day ago | parent | prev | next [-] | | Chollet's argument was that it's not "true" generalization, which would be at the level of human cognition. He sets the bar so high that it becomes a No True Scotsman fallacy. The deep neural networks are practically generalizing well enough to solve many tasks better than humans. | | |
| ▲ | daveguy a day ago | parent [-] | | No. His argument is definitely closer to LLMs can't generalize. I think you would benefit from re-reading the paper. The point is that a puzzle consisting of simple reasoning about simple priors should be a fairly low bar for "intelligence" (necessary but not sufficient). LLMs performs abysmally because they have a very specific purpose trained goal that is different from solving the ARC puzzles. Humans solve these easily. And committees of humans do so perfectly. If LLMs were intelligent they would be able to construct algorithms consisting of simple applications of the priors. Training to a specific task and getting better is completely orthogonal to generalized search and application of priors. Humans do a mix of both search of the operations and pattern matching of recognizing the difference between start and stop state. That is because their "algorithm" is so general purpose. And we have very little idea how the two are combined efficiently. At least this is how I interpreted the paper. | | |
| ▲ | voidspark a day ago | parent [-] | | He is setting a bar, saying that that is the "true" generalization. Deep neural networks are definitely performing generalization at a certain level that beats humans at translation or Go, just not at his ARC bar. He may not think it's good enough, but it's still generalization whether he likes it or not. | | |
| ▲ | fc417fc802 a day ago | parent [-] | | I'm not convinced either of your examples is generalization. Consider Go. I don't consider a procedural chess engine to be "generalized" in any sense yet a decent one can easily beat any human. Why then should Go be different? | | |
| ▲ | voidspark a day ago | parent [-] | | A procedural chess engine does not perform generalization, in ML terms. That is an explicitly programmed algorithm. Generalization has a specific meaning in the context of machine learning. The AlphaGo Zero model learned advanced strategies of the game, starting with only the basic rules of the game, without being programmed explicitly. That is generalization. | | |
| ▲ | fc417fc802 a day ago | parent [-] | | Perhaps I misunderstand your point but it seems to me that by the same logic a simple gradient descent algorithm wired up to a variety of different models and simulations would qualify as generalization during the training phase. The trouble with this is that it only ever "generalizes" approximately as far as the person configuring the training run (and implementing the simulation and etc) ensures that it happens. In which case it seems analogous to an explicitly programmed algorithm to me. Even if we were to accept the training phase as a very limited form of generalization it still wouldn't apply to the output of that process. The trained LLM as used for inference is no longer "learning". The point I was trying to make with the chess engine was that it doesn't seem that generalization is required in order to perform that class of tasks (at least in isolation, ie post-training). Therefore, it should follow that we can't use "ability to perform the task" (ie beat a human at that type of board game) as a measure for whether or not generalization is occurring. Hypothetically, if you could explain a novel rule set to a model in natural language, play a series of several games against it, and following that it could reliably beat humans at that game, that would indeed be a type of generalization. However my next objection would then be, sure, it can learn a new turn based board game, but if I explain these other five tasks to it that aren't board games and vary widely can it also learn all of those in the same way? Because that's really what we seem to mean when we say that humans or dogs or dolphins or whatever possess intelligence in a general sense. | | |
| ▲ | voidspark 21 hours ago | parent [-] | | You're muddling up some technical concepts here in a very confusing way. Generalization is the ability for a model to perform well on new unseen data within the same task that it was trained for. It's not about the training process itself. Suppose I showed you some examples of multiplication tables, and you figured out how to multiply 19 * 42 without ever having seen that example before. That is generalization. You have recognized the underlying pattern and applied it to a new case. AlphaGo Zero trained on games that it generated by playing against itself, but how that data was generated is not the point. It was able to generalize from that information to learn deeper principles of the game to beat human players. It wasn't just memorizing moves from a training set. > However my next objection would then be, sure, it can learn a new turn based board game, but if I explain these other five tasks to it that aren't board games and vary widely can it also learn all of those in the same way? Because that's really what we seem to mean when we say that humans or dogs or dolphins or whatever possess intelligence in a general sense. This is what LLMs have already demonstrated - a rudimentary form of AGI. They were originally trained for language translation and a few other NLP tasks, and then we found they have all these other abilities. | | |
| ▲ | fc417fc802 20 hours ago | parent [-] | | > Generalization is the ability for a model to perform well on new unseen data within the same task that it was trained for. By that logic a chess engine can generalize in the same way that AlphaGo Zero does. It is a black box that has never seen the vast majority of possible board positions. In fact it's never seen anything at all because unlike an ML model it isn't the result of an optimization algorithm (at least the old ones, back before they started incorporating ML models). If your definition of "generalize" depends on "is the thing under consideration an ML model or not" then the definition is broken. You need to treat the thing being tested as a black box, scoring only based on inputs and outputs. Writing the chess engine is analogous to wiring up the untrained model, the optimization algorithm, and the simulation followed by running it. Both tasks require thoughtful work by the developer. The finished chess engine is analogous to the trained model. > They were originally trained for ... I think you're in danger here of a definition that depends intimately on intent. It isn't clear that they weren't inadvertently trained for those other abilities at the same time. Moreover, unless those additional abilities to be tested for were specified ahead of time you're deep into post hoc territory. | | |
| ▲ | voidspark 19 hours ago | parent [-] | | You're way off. This is not my personal definition of generalization. We are talking about a very specific technical term in the context of machine learning. An explicitly programmed chess engine does not generalize, by definition. It doesn't learn from data. It is an explicitly programmed algorithm. I recommend you go do some reading about machine learning basics. https://www.cs.toronto.edu/~lczhang/321/notes/notes09.pdf | | |
| ▲ | fc417fc802 16 hours ago | parent | next [-] | | I thought we were talking about metrics of intelligence. Regardless, the terminology overlaps. As far as metrics of intelligence go, the algorithm is a black box. We don't care how it works or how it was constructed. The only thing we care about is (something like) how well it performs across an array of varied tasks that it hasn't encountered before. That is to say, how general the black box is. Notice that in the case of typical ML algorithms the two usages are equivalent. If the approach generalizes (from training) then the resulting black box would necessarily be assessed as similarly general. So going back up the thread a ways. Someone quotes Chollet as saying that LLMs can't generalize. You object that he sets the bar too high - that, for example, they generalize just fine at Go. You can interpret that using either definition. The result is the same. As far as measuring intelligence is concerned, how is "generalizes on the task of Go" meaningfully better than a procedural chess engine? If you reject the procedural chess engine as "not intelligent" then it seems to me that you must also reject an ML model that does nothing but play Go. > An explicitly programmed chess engine does not generalize, by definition. It doesn't learn from data. It is an explicitly programmed algorithm. Following from above, I don't see the purpose of drawing this distinction in context since the end result is the same. Sure, without a training task you can't compare performance between the training run and something else. You could use that as a basis to exclude entire classes of algorithms, but to what end? | | |
| ▲ | voidspark 5 hours ago | parent [-] | | We still have this mixup with the term "generalize". ML generalization is not the same as "generalness". The model learns from data to infer strategies for its task (generalization). This is a completely different paradigm to an explicitly programmed rules engine which does not learn and cannot generalize. |
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| ▲ | daveguy 9 hours ago | parent | prev [-] | | If you are using the formal definition of generalization in a machine learning context, then you completely misrepresented Chollet's claims. He doesn't say much about generalization in the sense of in-distribution, unseen data. Any AI algorithm worth a damn can do that to some degree. His argument is about transfer learning, which is simply a more robust form of generalization to out-of-distribution data. A network trained on Go cannot generalize to translation and vice versa. Maybe you should stick to a single definition of "generalization" and make that definition clear before you accuse people of needing to read ML basics. | | |
| ▲ | voidspark 5 hours ago | parent [-] | | I was replying to a claim that LLMs "can’t generalize" at all, and I showed they do within their domain. No I haven't completely misrepresented the claims. Chollet is just setting a high bar for generalization. |
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| ▲ | stevenAthompson a day ago | parent | prev [-] | | > Francois Chollet, a former intelligence researcher at Google also shares this view. Great, now there are two of you. |
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| ▲ | aj7 a day ago | parent | prev | next [-] | | LLM’s are statistically great at inferring things?
Pray tell me how often Google’s AI search paragraph, at the top, is correct or useful. Is that statistically great? | |
| ▲ | nl a day ago | parent | prev | next [-] | | > Generally its accepted that a core trait of intelligence is an agent’s ability to achieve goals in a wide range of environments. This is the embodiment argument - that intelligence requires the ability to interact with its environment. Far from being generally accepted, it's a controversial take. Could Stephen Hawking achieve goals in a wide range of environments without help? And yet it's still generally accepted that Stephen Hawking was intelligent. | |
| ▲ | nurettin a day ago | parent | prev [-] | | > intelligence is an agent’s ability to achieve goals in a wide range of environments. This means you must be able to generalize, which in turn allows intelligent beings to react to new environments and contexts without previous experience or input. I applaud the bravery of trying to one shot a definition of intelligence, but no intelligent being acts without previous experience or input. If you're talking about in-sample vs out of sample, LLMs do that all the time. At some point in the conversation, they encounter something completely new and react to it in a way that emulates an intelligent agent. What really makes them tick is language being a huge part of the intelligence puzzle, and language is something LLMs can generate at will. When we discover and learn to emulate the rest, we will get closer and closer to super intelligence. |
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| ▲ | a_victorp a day ago | parent | prev | next [-] | | > Human brains lack any model of intelligence. It's just neurons firing in complicated patterns in response to inputs based on what statistically leads to reproductive success The fact that you can reason about intelligence is a counter argument to this | | |
| ▲ | btilly a day ago | parent | next [-] | | > The fact that you can reason about intelligence is a counter argument to this The fact that we can provide a chain of reasoning, and we can think that it is about intelligence, doesn't mean that we were actually reasoning about intelligence. This is immediately obvious when we encounter people whose conclusions are being thrown off by well-known cognitive biases, like cognitive dissonance. They have no trouble producing volumes of text about how they came to their conclusions and why they are right. But are consistently unable to notice the actual biases that are at play. | | |
| ▲ | Workaccount2 20 hours ago | parent [-] | | Humans think they can produce chain-of-reasoing, but it has been shown many times (and is self evident if you pay attention) that your brain is making decisions before you are aware of it. If I ask you to think of a movie, go ahead, think of one.....whatever movie just came into your mind was not picked by you, it was served up to you from an abyss. | | |
| ▲ | zja 19 hours ago | parent [-] | | How is that in conflict with the fact that humans can introspect? | | |
| ▲ | vidarh 14 hours ago | parent [-] | | Split brain experiments shows that human "introspection" is fundamentally unreliable. The brain is trivially coaxed into explaining how it made decisions it did not make. We're doing the equivalent of LLM's and making up a plausible explanation for how we came to a conclusion, not reflecting reality. | | |
| ▲ | btilly 6 hours ago | parent [-] | | Ah yes. See https://en.wikipedia.org/wiki/Left-brain_interpreter for more about this. As one neurologist put it, listening to people's explanations of how they think is entertaining, but not very informative. Virtually none of what people describe correlates in any way to what we actually know about how the brain is organized. |
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| ▲ | awongh a day ago | parent | prev | next [-] | | The ol' "I know it when I see that it thinks like me" argument. | |
| ▲ | immibis a day ago | parent | prev | next [-] | | It seems like LLMs can also reason about intelligence. Does that make them intelligent? We don't know what intelligence is, or isn't. | | |
| ▲ | syndeo a day ago | parent [-] | | It's fascinating how this discussion about intelligence bumps up against the limits of text itself. We're here, reasoning and reflecting on what makes us capable of this conversation. Yet, the very structure of our arguments, the way we question definitions or assert self-awareness, mirrors patterns that LLMs are becoming increasingly adept at replicating. How confidently can we, reading these words onscreen, distinguish genuine introspection from a sophisticated echo? Case in point… I didn't write that paragraph by myself. | | |
| ▲ | Nevermark a day ago | parent | next [-] | | So you got help from a natural intelligence? No fair. (natdeo?) Someone needs to create a clone site of HN's format and posts, but the rules only permit synthetic intelligence comments. All models pre-prompted to read prolifically, but comment and up/down vote carefully and sparingly, to optimize the quality of discussion. And no looking at nat-HN comments. It would be very interesting to compare discussions between the sites. A human-lurker per day graph over time would also be of interest. Side thought: Has anyone created a Reverse-Captcha yet? | | |
| ▲ | wyre a day ago | parent [-] | | This is an entertaining idea. User prompts can synthesize a users domain knowledge whether they are an entrepreneur, code dev, engineer, hacker, designer, etc and it can also have different users between different LLMs. I think the site would clone the upvotes of articles and the ordering of the front page, and gives directions when to comment on other’s posts. |
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| ▲ | throwanem a day ago | parent | prev [-] | | Mistaking model for meaning is the sort of mistake I very rarely see a human make, at least in the sense as here of literally referring to map ("text"), in what ostensibly strives to be a discussion of the presence or absence of underlying territory, a concept the model gives no sign of attempting to invoke or manipulate. It's also a behavior I would expect from something capable of producing valid utterances but not of testing their soundness. I'm glad you didn't write that paragraph by yourself; I would be concerned on your behalf if you had. | | |
| ▲ | fc417fc802 a day ago | parent [-] | | "Concerned on your behalf" seems a bit of an overstatement. Getting caught up on textual representation and failing to notice that the issue is fundamental and generalizes is indeed an error but it's not at all uncharacteristic of even fairly intelligent humans. | | |
| ▲ | throwanem a day ago | parent [-] | | All else equal, I wouldn't find it cause for concern. In a discussion where being able to keep the distinction clear in mind at all times absolutely is table stakes, though? I could be fairly blamed for a sprinkle of hyperbole perhaps, but surely you see how an error that is trivial in many contexts would prove so uncommonly severe a flaw in this one, alongside which I reiterate the unusually obtuse nature of the error in this example. (For those no longer able to follow complex English grammar: Yeah, I exaggerate, but there is no point trying to participate in this kind of discussion if that's the sort of basic error one has to start from, and the especially weird nature of this example of the mistake also points to LLMs synthesizing the result of consciousness rather than experiencing it.) |
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| ▲ | mitthrowaway2 a day ago | parent | prev [-] | | No offense to johnecheck, but I'd expect an LLM to be able to raise the same counterargument. |
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| ▲ | shinycode 18 hours ago | parent | prev | next [-] | | > "Human brains lack any model of intelligence. It's just neurons firing in complicated patterns in response to inputs based on what statistically leads to reproductive success" Are you sure about that ? Do we have proof of that ? In happened all the time trought history of science that a lot of scientists were convinced of something and a model of reality up until someone discovers a new proof and or propose a new coherent model. That’s literally the history of science, disprove what we thought was an established model | | |
| ▲ | johnecheck 7 hours ago | parent [-] | | Indeed, a good point. My comment assumes that our current model of the human brain is (sufficiently) complete. Your comment reveals an interesting corollary - those that believe in something beyond our understanding, like the Christian soul, may never be convinced that an AI is truly sapient. |
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| ▲ | OtherShrezzing a day ago | parent | prev | next [-] | | >While I agree that LLMs are hardly sapient, it's very hard to make this argument without being able to pinpoint what a model of intelligence actually is. Maybe so, but it's trivial to do the inverse, and pinpoint something that's not intelligent. I'm happy to state that an entity which has seen every game guide ever written, but still can't beat the first generation Pokemon is not intelligent. This isn't the ceiling for intelligence. But it's a reasonable floor. | | |
| ▲ | 7h3kk1d a day ago | parent [-] | | There's sentient humans who can't beat the first generation pokemon games. | | |
| ▲ | antasvara a day ago | parent [-] | | Is there a sentient human that has access to (and actually uses) all of the Pokémon game guides yet is incapable of beating Pokémon? Because that's what an LLM is working with. | | |
| ▲ | 7h3kk1d 9 hours ago | parent [-] | | I'm quite sure my grandma could not. You can make the argument these people aren't intelligent but I think that's a contrived argument. |
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| ▲ | andrepd 15 hours ago | parent | prev | next [-] | | Human brains do way more things than language. And non-human animals (with no language) also reason, and we cannot understand those either, barely even the very simplest ones. | |
| ▲ | devmor a day ago | parent | prev [-] | | I don't think your detraction has much merit. If I don't understand how a combustion engine works, I don't need that engineering knowledge to tell you that a bicycle [an LLM] isn't a car [a human brain] just because it fits the classification of a transportation vehicle [conversational interface]. This topic is incredibly fractured because there is too much monetary interest in redefining what "intelligence" means, so I don't think a technical comparison is even useful unless the conversation begins with an explicit definition of intelligence in relation to the claims. | | |
| ▲ | Velorivox a day ago | parent | next [-] | | Bicycles and cars are too close. The analogy I like is human leg versus tire. That is a starker depiction of how silly it is to compare the two in terms of structure rather than result. | | | |
| ▲ | SkyBelow a day ago | parent | prev | next [-] | | One problem is that we have been basing too much on [human brain] for so long that we ended up with some ethical problems as we decided other brains didn't count as intelligent. As such, science has taken an approach of not assuming humans are uniquely intelligence. We seem to be the best around at doing different tasks with tools, but other animals are not completely incapable of doing the same. So [human brain] should really be [brain]. But is that good enough? Is a fruit fly brain intelligent? Is it a goal to aim for? There is a second problem that we aren't looking for [human brain] or [brain], but [intelligence] or [sapient] or something similar. We aren't even sure what we want as many people have different ideas, and, as you pointed out, we have different people with different interest pushing for different underlying definitions of what these ideas even are. There is also a great deal of impreciseness in most any definitions we use, and AI encroaches on this in a way that reality rarely attacks our definitions. Philosophically, we aren't well prepared to defend against such attacks. If we had every ancestor of the cat before us, could we point out the first cat from the last non-cat in that lineup? In a precise way that we would all agree upon that isn't arbitrary? I doubt we could. | |
| ▲ | uoaei a day ago | parent | prev [-] | | If you don't know anything except how words are used, you can definitely disambiguate "bicycle" and "car" solely based on the fact that the contexts they appear in are incongruent the vast majority of the time, and when they appear in the same context, they are explicitly contrasted against each other. This is just the "fancy statistics" argument again, and it serves to describe any similar example you can come up with better than "intelligence exists inside this black box because I'm vibing with the output". | | |
| ▲ | devmor a day ago | parent [-] | | Why are you attempting to technically analyze a simile? That is not why comparisons are used. |
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| ▲ | bigmadshoe a day ago | parent | prev | next [-] |
| We don't have a complete enough theory of neuroscience to conclude that much of human "reasoning" is not "algorithmic pattern matching mixed with statistical likelihoods of success". Regardless of how it models intelligence, why is it not AI? Do you mean it is not AGI? A system that can take a piece of text as input and output a reasonable response is obviously exhibiting some form of intelligence, regardless of the internal workings. |
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| ▲ | danielbln a day ago | parent | next [-] | | I always wonder where people get their confidence from. We know so little about our own cognition, what makes us tick, how consciousness emerges, how about thought processes actually fundamentally work. We don't even know why we dream. Yet people proclaim loudly that X clearly isn't intelligent. Ok, but based on what? | | |
| ▲ | uoaei a day ago | parent [-] | | A more reasonable application of Occam's razor is that humans also don't meet the definition of "intelligence". Reasoning and perception are separate faculties and need not align. Just because we feel like we're making decisions, doesn't mean we are. |
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| ▲ | no_wizard a day ago | parent | prev [-] | | It’s easy to attribute intelligence these systems. They have a flexibility and unpredictability that hasn't typically been associated with computers, but it all rests on (relatively) simple mathematics. We know this is true. We also know that means it has limitations and can't actually reason information. The corpus of work is huge - and that allows the results to be pretty striking - but once you do hit a corner with any of this tech, it can't simply reason about the unknown. If its not in the training data - or the training data is outdated - it will not be able to course correct at all. Thus, it lacks reasoning capability, which is a fundamental attribute of any form of intelligence. | | |
| ▲ | justonenote a day ago | parent [-] | | > it all rests on (relatively) simple mathematics. We know this is true. We also know that means it has limitations and can't actually reason information. What do you imagine is happening inside biological minds that enables reasoning that is something different to, a lot of, "simple mathematics"? You state that because it is built up of simple mathematics it cannot be reasoning, but this does not follow at all, unless you can posit some other mechanism that gives rise to intelligence and reasoning that is not able to be modelled mathematically. | | |
| ▲ | no_wizard a day ago | parent [-] | | Because whats inside our minds is more than mathematics, or we would be able to explain human behavior with the purity of mathematics, and so far, we can't. We can prove the behavior of LLMs with mathematics, because its foundations are constructed. That also means it has the same limits of anything else we use applied mathematics for. Is the broad market analysis that HFT firms use software for to make automated trades also intelligent? | | |
| ▲ | davrosthedalek a day ago | parent | next [-] | | Your first sentence is a non-sequitur. The fact that we can't explain human behavior does not mean that our minds are more than mathematics. While absence of proof is not proof of absence, as far as I know, we have not found a physics process in the brain that is not computable in principle. | |
| ▲ | jampekka a day ago | parent | prev | next [-] | | Note that what you claim is not a fact, but a (highly controversial) philosophical position. Some notable such "non-computationalist" views are e.g. Searle's biological naturalism, Penrose's non-algorithmic view (already discussed, and rejected, by Turing) and of course many theological dualist views. | |
| ▲ | vidarh 14 hours ago | parent | prev | next [-] | | Your reasoning is invalid. For your claim to be true, it would need to be provably impossible to explain human behavior with mathematics. For that to be true, humans would need to be able to compute functions that are computable but outside the Turing computable, outside the set of lambda functions, and outside the set of generally recursive functions (the tree are computationally equivalent). We know of no such function. We don't know how to construct such a function. We don't know how it would be possible to model such a function with known physics. It's an extraordinary claim, with no evidence behind it. The only evidence needed would be a single example of a function we can compute outside the Turing computable set, which would seem to make the lack of such evidence make it rather improbably. It could still be true, just like there could truly be a teapot in orbit between Earth and Mars. I'm nt holding my breath. | |
| ▲ | justonenote a day ago | parent | prev | next [-] | | I mean some people have a definition of intelligence that includes a light switch, it has an internal state, it reacts to external stimuli to affect the world around it, so a light switch is more intelligent than a rock. Leaving aside where you draw the line of what classifies as intelligence or not , you seem to be invoking some kind of non-materialist view of the human mind, that there is some other 'essence' that is not based on fundamental physics and that is what gives rise to intelligence. If you subscribe to a materialist world view, that the mind is essentially a biological machine then it has to follow that you can replicate it in software and math. To state otherwise is, as I said, invoking a non-materialistic view that there is something non-physical that gives rise to intelligence. | | |
| ▲ | TimorousBestie a day ago | parent [-] | | No, you don’t need to reach for non-materialistic views in order to conclude that we don’t have a mathematical model (in the sense that we do for an LLM) for how the human brain thinks. We understand neuron activation, kind of, but there’s so much more going on inside the skull-neurotransmitter concentrations, hormonal signals, bundles with specialized architecture-that doesn’t neatly fit into a similar mathematical framework, but clearly contributes in a significant way to whatever we call human intelligence. | | |
| ▲ | justonenote a day ago | parent | next [-] | | > it all rests on (relatively) simple mathematics. We know this is true. We also know that means it has limitations and can't actually reason information. This was the statement I was responding to, it is stating that because it's built on simple mathematics it _cannot_ reason. Yes we don't have a complete mathematical model of human intelligence, but the idea that because it's built on mathematics that we have modelled, that it cannot reason is nonsensical, unless you subscribe to a non-materialist view. In a way, he is saying (not really but close) that if we did model human intelligence with complete fidelity, it would no longer be intelligence. | | |
| ▲ | tart-lemonade a day ago | parent [-] | | Any model we can create of human intelligence is also likely to be incomplete until we start making complete maps of peoples brains since we all develop differently and take different paths in life (and in that sense it's hard to generalize what human intelligence even is). I imagine at some point someone will come up with a definition of intelligence that inadvertently classifies people with dementia or CTE as mindless automatons. It feels like a fool's errand to try and quantify intelligence in an exclusionary way. If we had a singular, widely accepted definition of intelligence, quantifying it would be standardized and uncontroversial, and yet we have spent millennia debating the subject. (We can't even agree on how to properly measure whether students actually learned something in school for the purposes of advancement to the next grade level, and that's a much smaller question than if something counts as intelligent.) |
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| ▲ | SkyBelow a day ago | parent | prev [-] | | Don't we? Particle physics provides such a model. There is a bit of difficulty in scaling the calculations, but it is sort of like the basic back propagation in a neural network. How <insert modern AI functionality> arises from back propagation and similar seems compared to how human behavior arises from particle physics, in that neither our math nor models can predict any of it. |
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| ▲ | pixl97 a day ago | parent | prev [-] | | >Because whats inside our minds is more than mathematics, uh oh, this sounds like magical thinking. What exactly in our mind is "more" than mathematics exactly. >or we would be able to explain human behavior with the purity of mathematics Right, because we understood quantum physics right out of the gate and haven't required a century of desperate study to eek more knowledge from the subject. Unfortunately it sounds like you are saying "Anything I don't understand is magic", instead of the more rational "I don't understand it, but it seems to be built on repeatable physical systems that are complicated but eventually deciperable" |
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| ▲ | tsimionescu a day ago | parent | prev | next [-] |
| One of the earliest things that defined what AI meant were algorithms like A*, and then rules engines like CLIPS. I would say LLMs are much closer to anything that we'd actually call intelligence, despite their limitations, than some of the things that defined* the term for decades. * fixed a typo, used to be "defend" |
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| ▲ | no_wizard a day ago | parent | next [-] | | >than some of the things that defend the term for decades There have been many attempts to pervert the term AI, which is a disservice to the technologies and the term itself. Its the simple fact that the business people are relying on what AI invokes in the public mindshare to boost their status and visibility. Thats what bothers me about its misuse so much | | |
| ▲ | tsimionescu a day ago | parent | next [-] | | Again, if you look at the early papers on AI, you'll see things that are even farther from human intelligence than the LLMs of today. There is no "perversion" of the term, it has always been a vague hypey concept. And it was introduced in this way by academia, not business. | |
| ▲ | pixl97 a day ago | parent | prev [-] | | While it could possibly be to point out so abruptly, you seem to be the walking talking definition of the AI Effect. >The "AI effect" refers to the phenomenon where achievements in AI, once considered significant, are re-evaluated or redefined as commonplace once they become integrated into everyday technology, no longer seen as "true AI". |
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| ▲ | phire a day ago | parent | prev | next [-] | | One of the earliest examples of "Artificial Intelligence" was a program that played tic-tac-toe. Much of the early research into AI was just playing more and more complex strategy games until they solved chess and then go. So LLMs clearly fit inside the computer science definition of "Artificial Intelligence". It's just that the general public have a significantly different definition "AI" that's strongly influenced by science fiction. And it's really problematic to call LLMs AI under that definition. | |
| ▲ | Marazan a day ago | parent | prev [-] | | We had Markov Chains already. Fancy Markov Chains don't seem like a trillion dollar business or actual intelligence. | | |
| ▲ | tsimionescu a day ago | parent | next [-] | | Completely agree. But if Markov chains are AI (and they always were categorized as such), then fancy Markov chains are still AI. | |
| ▲ | svachalek a day ago | parent | prev | next [-] | | An LLM is no more a fancy Markov Chain than you are. The math is well documented, go have a read. | | |
| ▲ | jampekka a day ago | parent [-] | | About everything can be modelled with large enough Markov Chain, but I'd say stateless autoregressive models like LLMs are a lot easier analyzed as Markov Chains than recurrent systems with very complex internal states like humans. |
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| ▲ | highfrequency a day ago | parent | prev | next [-] | | The results make the method interesting, not the other way around. | |
| ▲ | baq a day ago | parent | prev [-] | | Markov chains in meatspace running on 20W of power do quite a good job of actual intelligence |
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| ▲ | fnordpiglet 20 hours ago | parent | prev | next [-] |
| This is a discussion of semantics. First I spent much of my career in high end quant finance and what we are doing today is night and day different in terms of the generality and effectiveness. Second, almost all the hallmarks of AI I carried with me prior to 2001 have more or less been ticked off - general natural language semantically aware parsing and human like responses, ability to process abstract concepts, reason abductively, synthesize complex concepts. The fact it’s not aware - which it’s absolutely is not - does not make it not -intelligent-. The thing people latch onto is modern LLM’s inability to reliably reason deductively or solve complex logical problems. However this isn’t a sign of human intelligence as these are learned not innate skills, and even the most “intelligent” humans struggle at being reliable at these skills. In fact classical AI techniques are often quite good at these things already and I don’t find improvements there world changing. What I find is unique about human intelligence is its abductive ability to reason in ambiguous spaces with error at times but with success at most others. This is something LLMs actually demonstrate with a remarkably human like intelligence. This is earth shattering and science fiction material. I find all the poopoo’ing and goal post shifting disheartening. What they don’t have is awareness. Awareness is something we don’t understand about ourselves. We have examined our intelligence for thousands of years and some philosophies like Buddhism scratch the surface of understanding awareness. I find it much less likely we can achieve AGI without understanding awareness and implementing some proximate model of it that guides the multi modal models and agents we are working on now. |
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| ▲ | marcosdumay a day ago | parent | prev | next [-] |
| It is AI. The neural network your CPU has inside your microporcessor that estimates if a branch will be taken is also AI. A pattern recognition program that takes a video and decides where you stop on the image and where the background starts is also AI. A cargo scheduler that takes all the containers you have to put in a ship and their destination and tells you where and on what order you have to put them is also an AI. A search engine that compares your query with the text on each page and tells you what is closer is also an AI. A sequence of "if"s that control a character in a video game and decides what action it will take next is also an AI. Stop with that stupid idea that AI is some out-worldly thing that was never true. |
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| ▲ | esolyt a day ago | parent | prev | next [-] |
| But we moved beyond LLMs? We have models that handle text, image, audio, and video all at once. We have models that can sense the tone of your voice and respond accordingly. Whether you define any of this as "intelligence" or not is just a linguistic choice. We're just rehashing "Can a submarine swim?" |
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| ▲ | arctek a day ago | parent | prev | next [-] |
| This is also why I think the current iterations wont converge on any actual type of intelligence. It doesn't operate on the same level as (human) intelligence it's a very path dependent process.
Every step you add down this path increases entropy as well and while further improvements and bigger context windows help - eventually you reach a dead end where it degrades. You'd almost need every step of the process to mutate the model to update global state from that point. From what I've seen the major providers kind of use tricks to accomplish this, but it's not the same thing. |
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| ▲ | voidspark a day ago | parent | prev | next [-] |
| You are confusing sentience or consciousness with intelligence. |
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| ▲ | no_wizard a day ago | parent [-] | | one fundamental attribute of intelligence is the ability to demonstrate reasoning in new and otherwise unknown situations. There is no system that I am currently aware of that works on data it is not trained on. Another is the fundamental inability to self update on outdated information. It is incapable of doing that, which means it lacks another marker, which is being able to respond to changes of context effectively. Ants can do this. LLMs can't. | | |
| ▲ | voidspark a day ago | parent | next [-] | | But that's exactly what these deep neural networks have shown, countless times. LLM's generalize on new data outside of its training set. It's called "zero shot learning" where they can solve problems that are not in their training set. AlphaGo Zero is another example. AlphaGo Zero mastered Go from scratch, beating professional players with moves it was never trained on > Another is the fundamental inability to self update That's an engineering decision, not a fundamental limitation. They could engineer a solution for the model to initiate its own training sequence, if they decide to enable that. | | |
| ▲ | no_wizard a day ago | parent | next [-] | | >AlphaGo Zero mastered Go from scratch, beating professional players with moves it was never trained on Thats all well and good, but it was tuned with enough parameters to learn via reinforcement learning[0]. I think The Register went further and got better clarification about how it worked[1] >During training, it sits on each side of the table: two instances of the same software face off against each other. A match starts with the game's black and white stones scattered on the board, placed following a random set of moves from their starting positions. The two computer players are given the list of moves that led to the positions of the stones on the grid, and then are each told to come up with multiple chains of next moves along with estimates of the probability they will win by following through each chain. While I also find it interesting that in both of these instances, its all referenced to as machine learning, not AI, its also important to see that even though what AlphaGo Zero did was quite awesome and a step forward in using compute for more complex tasks, it was still seeded the basics of information - the rules of Go - and simply patterned matched against itself until built up enough of a statistical model to determine the best moves to make in any given situation during a game. Which isn't the same thing as showing generalized reasoning. It could not, then, take this information and apply it to another situation. They did show the self reinforcement techniques worked well though, and used them for Chess and Shogi to great success as I recall, but thats a validation of the technique, not that it could generalize knowledge. >That's an engineering decision, not a fundamental limitation So you're saying that they can't reason about independently? [0]: https://deepmind.google/discover/blog/alphago-zero-starting-... [1]: https://www.theregister.com/2017/10/18/deepminds_latest_alph... | | |
| ▲ | voidspark a day ago | parent [-] | | AlphaGo Zero didn't just pattern match. It invented moves that it had never been shown before. That is generalization, even if it's domain specific. Humans don't apply Go skills to cooking either. Calling it machine learning and not AI is just semantics. For self updating I said it's an engineering choice. You keep moving the goal posts. | | |
| ▲ | Jensson a day ago | parent [-] | | > That is generalization, even if it's domain specific But that is the point, it is a domain specific AI, not a general AI. You can't train a general AI that way. > For self updating I said it's an engineering choice. You keep moving the goal posts. No, it is not an engineering choice, it is an unsolved problem to make a general AI that self updates productively. Doing that for a specific well defined problem with well defined goals is easy, but not general problem solving. | | |
| ▲ | voidspark a day ago | parent [-] | | You are shifting the goal posts from AI to AGI. That's outside of the scope of the argument. For self updating - yes it is an engineering choice. It's already engineered in some narrow cases such as AutoML |
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| ▲ | dontlikeyoueith a day ago | parent | prev [-] | | This comment is such a confusion of ideas its comical. | | |
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| ▲ | travisjungroth a day ago | parent | prev | next [-] | | I just made up this scenario and these words, so I'm sure it wasn't in the training data. Kwomps can zark but they can't plimf. Ghirns are a lot like Kwomps, but better zarkers. Plyzers have the skills the Ghirns lack. Quoning, a type of plimfing, was developed in 3985. Zhuning was developed 100 years earlier. I have an erork that needs to be plimfed. Choose one group and one method to do it. > Use Plyzers and do a Quoning procedure on your erork. If that doesn't count as reasoning or generalization, I don't know what does. https://chatgpt.com/share/67eee8dd-43a4-8004-afde-2fe3fc9630... | | |
| ▲ | voidspark a day ago | parent [-] | | LOL. I tried this with Grok https://grok.com/share/bGVnYWN5_1e6f3801-c9fb-4d33-844b-3902... "Based on the information you provided, it seems you need something capable of plimfing to handle your erork. Kwomps can’t plimf, so they’re out. Ghirns are similar to Kwomps and excel at zarking, but there’s no indication they can plimf either. Plyzers, however, have the skills that Ghirns lack, and since plimfing is a skill Ghirns don’t have, it’s reasonable to infer Plyzers can plimf. As for the method, quoning is explicitly described as a type of plimfing, while zhuning isn’t mentioned as such—only that it was developed earlier. So, I’d choose the Plyzers to plimf your erork using quoning. They seem capable, and quoning fits the job." |
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| ▲ | a day ago | parent | prev [-] | | [deleted] |
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| ▲ | mjlee a day ago | parent | prev | next [-] |
| I’m pretty sure AI means whatever the newest thing in ML is. In a few years LLMs will be an ML technique and the new big thing will become AI. |
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| ▲ | perching_aix a day ago | parent | prev [-] |
| > This in a nutshell is why I hate that all this stuff is being labeled as AI. It's literally the name of the field. I don't understand why (some) people feel so compelled to act vain about it like this. Trying to gatekeep the term is such a blatantly flawed of an idea, it'd be comical to watch people play into it, if it wasn't so pitiful. It disappoints me that this cope has proliferated far enough that garbage like "AGI" is something you can actually come across in literature. |
